• 제목/요약/키워드: Multi-Period Output Model

검색결과 20건 처리시간 0.023초

일관된 지연 효과를 고려한 다기간 DEA 모형 (A Multi-Period Input DEA Model with Consistent Time Lag Effects)

  • 정병호;장연상;이태한
    • 산업경영시스템학회지
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    • 제42권3호
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    • pp.8-14
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    • 2019
  • Most of the data envelopment analysis (DEA) models evaluate the relative efficiency of a decision making unit (DMU) based on the assumption that inputs in a specific period are consumed to produce the output in the same period of time. However, there may be some time lag between the consumption of input resources and the production of outputs. A few models to handle the concept of the time lag effect have been proposed. This paper suggests a new multi-period input DEA model considering the consistent time lag effects. Consistency of time lag effect means that the time delay for the same input factor or output factor are consistent throughout the periods. It is more realistic than the time lag effect for the same output or input factor can vary over the periods. The suggested model is an output-oriented model in order to adopt the consistent time lag effect. We analyze the results of the suggested model and the existing multi period input model with a sample data set from a long-term national research and development program in Korea. We show that the suggested model may have the better discrimination power than existing model while the ranking of DMUs is not different by two nonparametric tests.

시간지연 효과를 고려한 기간 통합 DEA 모형의 개발 (Development of A Multi-Period Integration DEA Model Considering Time Lag Effect)

  • 장연상;정병호
    • 한국경영과학회지
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    • 제37권4호
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    • pp.37-50
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    • 2012
  • The existing DEA models have been devoted to evaluate relative efficiency of DMUs based on multiple input and output factors of a same period. However, a certain kind of lead time can be required to produce outputs using inputs in an organization. R&D evaluation is a typical area with this kinds of time lag. Thus, the purpose of this paper is to develop a new DEA model to deal with time lag effect in performance evaluation. The proposed model is to find relative efficiency of each DMU for each period considering the time lag effect. A case example using a real data set is also given to show the usage or implication of the suggested model. The results are compared with the ones of the CCR model and the multi-periods input model.

생성집합을 이용한 다 기간 성과평가를 위한 DEA 모델 개발 및 공학교육혁신사업 사례적용 (Multi-period DEA Models Using Spanning Set and A Case Example)

  • 김기성;이태한
    • 산업경영시스템학회지
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    • 제45권3호
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    • pp.57-65
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    • 2022
  • DEA(data envelopment analysis) is a technique for evaluation of relative efficiency of decision making units (DMUs) that have multiple input and output. A DEA model measures the efficiency of a DMU by the relative position of the DMU's input and output in the production possibility set defined by the input and output of the DMUs being compared. In this paper, we proposed several DEA models measuring the multi-period efficiency of a DMU. First, we defined the input and output data that make a production possibility set as the spanning set. We proposed several spanning sets containing input and output of entire periods for measuring the multi-period efficiency of a DMU. We defined the production possibility sets with the proposed spanning sets and gave DEA models under the production possibility sets. Some models measure the efficiency score of each period of a DMU and others measure the integrated efficiency score of the DMU over the entire period. For the test, we applied the models to the sample data set from a long term university student training project. The results show that the suggested models may have the better discrimination power than CCR based results while the ranking of DMUs is not different.

제한된 조건하에서의 최적생산-분배결정 모델에 관한 연구 (A study on optimization model for an industrial production-distribution problem with consideration of a restricted transportation time)

  • 임석진;김경섭;박면웅
    • 한국경영과학회:학술대회논문집
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    • 대한산업공학회/한국경영과학회 2002년도 춘계공동학술대회
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    • pp.463-468
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    • 2002
  • Recently, a multi-facility, multi-product and multi-period industrial problem has been widely investigated in Supply Chain Management(SCM). One of the key issues in the current SCM research area involves reducing both production and distribution costs. We have developed an optimization model to tackle the above problems under the restricted conditions such as transportation time and a zero inventory. The model can be used to deride an appropriate factory and assign an optimal output the factory yields. This paper deals with the main idea of the proposed methodology in depth.

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우리나라 수출의 고용파급효과에 관한 연구: 다지역산업연관 및 구조적 요인분해 분석을 중심으로 (Korea's Employment Embodied in Exports: a Multi-Regional Input-Output and Structural Decomposition Analysis)

  • 김태진
    • 경제분석
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    • 제26권4호
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    • pp.65-97
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    • 2020
  • 본 논문의 목적은 우리나라 수출의 고용파급효과와 그 변화 요인을 상세히 분석하는 데 있다. 이를 위해 가장 최근에 공표된 World Input-Output Database (WIOD)의 2000년부터 2014년까지의 세계산업연관표와 사회경제계정을 이용하여 다지역산업연관 및 구조적 요인 분해 분석을 실시하였다. 주요 분석 결과는 다음과 같다. 첫째, 우리나라 수출에 체화된 고용은 지속적으로 증가하였고, 우리나라 고용의 수출 의존도 역시 상승 추세를 보였다. 그러나 부가가치 수출의 고용유발계수는 전반적으로 하락하는 것으로 나타났다. 둘째, 우리나라 수출에 체화된 고용의 상당 부분은 중국, 미국, RoW(Rest of the World)의 최종수요에 기인한 것으로 분석되었다. 셋째, 우리나라 수출에 체화된 고용의 증대에 가장 큰 영향을 준 요인은 해외 최종수요의 변화 요인이었다. 이러한 실증분석 결과에 기초하여 우리나라의 국내 고용 확대를 위한 의미 있는 정책적 시사점을 논의하였다.

Identification of continuous time-delay systems using the genetic algorithm

  • Hachino, Tomohiro;Yang, Zi-Jiang;Tsuji, Teruo
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국제학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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    • pp.1-6
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    • 1993
  • This report proposes a novel method of identification of continuous time-delay systems from sampled input-output data. By the aid of a digital pre-filter, an approximated discrete-time estimation model is first derived, in which the system parameters remain in their original form and the time delay need not be an integral multiple of th sampling period. Then an identification method combining the common linear least squares(LS) method or the instrumental variable(IV) method with the genetic algorithm(GA) is proposed. That is, the time-delay is selected by the GA, and the system parameters are estimated by the LS or IV method. Furthermore, the proposed method is extended to the case of multi-input multi-output systems where the time-delays in the individual input channels may differ each other. Simulation resutls show that our method yields consistent estimates even in the presence of high measurement noises.

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다기간 자료포락분석을 이용한 전기차 충전소 효율성 변화 분석 (Analysis on the Efficiency Change in Electric Vehicle Charging Stations Using Multi-Period Data Envelopment Analysis)

  • 손동훈;강영수;김화중
    • 산업경영시스템학회지
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    • 제44권2호
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    • pp.1-14
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    • 2021
  • It is highly challenging to measure the efficiency of electric vehicle charging stations (EVCSs) because factors affecting operational characteristics of EVCSs are time-varying in practice. For the efficiency measurement, environmental factors around the EVCSs can be considered because such factors affect charging behaviors of electric vehicle drivers, resulting in variations of accessibility and attractiveness for the EVCSs. Considering dynamics of the factors, this paper examines the technical efficiency of 622 electric vehicle charging stations in Seoul using data envelopment analysis (DEA). The DEA is formulated as a multi-period output-oriented constant return to scale model. Five inputs including floating population, number of nearby EVCSs, average distance of nearby EVCSs, traffic volume and traffic congestion are considered and the charging frequency of EVCSs is used as the output. The result of efficiency measurement shows that not many EVCSs has most of charging demand at certain periods of time, while the others are facing with anemic charging demand. Tobit regression analyses show that the traffic congestion negatively affects the efficiency of EVCSs, while the traffic volume and the number of nearby EVCSs are positive factors improving the efficiency around EVCSs. We draw some notable characteristics of efficient EVCSs by comparing means of the inputs related to the groups classified by K-means clustering algorithm. This analysis presents that efficient EVCSs can be generally characterized with the high number of nearby EVCSs and low level of the traffic congestion.

다중브리지 PWM 인버터로 구성된 SSSC의 동특성 분석 (Dynamic Characteristics Analysis of Multi-bridge PWM Inverter SSSC)

  • 한병문;박덕희;김성남
    • 대한전기학회논문지:전기기기및에너지변환시스템부문B
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    • 제50권6호
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    • pp.296-302
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    • 2001
  • This paper proposes a SSSC based on multi-bridge inverters. The dynamic characteristic of the proposed SSSC was analyzed by EMTP simulation and a scaled hardware model, assuming that the SSSC is inserted in the transmission line of the one-machine-infinite-bus power system. The proposed SSSC has 6 multi-bridge inverters per phase, which generates 13 pulses for each half period of power frequency. The proposed SSSC generates a quasi-sinusoidal output voltage by 90 degree phase shift to the line current. The proposed SSSC does not require the coupling transformer for voltage injection, and has a flexibility in operation voltage by increasing the number of series connection.

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다중브리지 PWM 인버터로 구성된 SSSC의 동특성 분석 (Dynamic Characteristic Analysis of Multi-bridge PWM Inverter SSSC)

  • 배병렬;박상호;하요철;김희중;한병문;김현우
    • 전력전자학회:학술대회논문집
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    • 전력전자학회 2001년도 전력전자학술대회 논문집
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    • pp.685-688
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    • 2001
  • This paper proposes an SSSC based on multi-bridge inverters. The dynamic characteristic of the proposed SSSC was analyzed by EMTP simulation and a scaled handware model, assuming that the SSSC is inserted in the transmission line of the one-machine-infinite-bus power system. The proposed SSSC has 6 multi-bridge inverters per phase, which generates 13 pulses for each half period of power frequency. The proposed SSSC generates a quasi-simusoidal output voltage by 90 degree phase shift to the line current The proposed SSSC does not require the coupling transformer for voltage injection, and has a flexibility in operation voltage by increasing the number of series connection.

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신경망리론에 의한 다목적 저수지의 홍수유입량 예측 (Flood Inflow Forecasting on Multipurpose Reservoir by Neural Network)

  • 심순보;김만식
    • 한국수자원학회논문집
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    • 제31권1호
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    • pp.45-57
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    • 1998
  • 본 논문의 목적은 다목적 저수지의 홍수유입량 예측을 위한 방법으로 병렬다중결선의 계층구조를 가진 신경망이론에 의하여 홍수시 불확실한 비선형시스템의 특성을 같는 저수지 유입량 예측모형을 개발하는 것이다. 신경망이론을 이용한 예측모형의 개발을 위하여 역전파 학습알고리즘을 사용하였으며 역전파 학습알고리즘 사용시 흔히 대두되는 지역최소값 문제와 수렴속도의 향상을 위해서 최적화기법인 경사하강법을 이용한 모멘트법과 경사하강법과 Gauss-Newton 방법을 이용한 Leverberg-Marquardt 법을 사용하였다. 모형개발에 사용된 자료는 연속적인 값으로 입력자료와 출력자료를 강우와 댐유입량을 학습시킨 후, 저수지의 홍수유입량 예측을 위한 다층신경망 모형을 구성하였다. 학습시 사용한 자료를 토대로 개발된 모형을 검정한 결과 매우 만족스런 결과를 얻을 수 있었고 실제 충주댐 유역을 대상으로 저수지 홍수유입량 예측결과 모형의 타당성을 입증할 수 있었다.

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